fMRI is a unique non-invasive approach for understanding the functional organization of the human brain, and task-based fMRI promotes identification of functionally relevant brain regions associated with a given task. Here, we use fMRI (using the Poffenberger Paradigm) data collected in mono- and dizygotic twin pairs t…
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48 results for “twin networks”
Generative models create personalized patient health simulations.
problem Creating accurate digital twins for personalized medicine.
method Neural network architecture for conditional generative models of clinical trajectories.
result Same architecture generates accurate twins across 13 indications.
The paper introduces a method to decompose variance in twin networks for better treatment effect estimation.
problem Accurate treatment effect estimation requires reliable uncertainty measures to locate model failures.
method Layer-wise variance decomposition using Monte Carlo Dropout in twin networks.
result The encoder component dominates under distributional shift, providing a practical diagnostic for data collection.
Multiple Sclerosis (MS) is a neurodegenerative disorder characterized by a complex set of clinical assessments. We use an unsupervised machine learning model called a Conditional Restricted Boltzmann Machine (CRBM) to learn the relationships between covariates commonly used to characterize subjects and their disease pr…
This paper explores Brunnian twin groups and their properties.
problem Alexander-Markov correspondence for isotopy classes of immersed circles.
method Diagrammatic representations and combinatorial structures.
result Brunnian twin groups are free groups when more than two strands are involved.
Twin neural network regression predicts differences between two data points.
problem Traditional regression methods are inaccurate for certain data sets.
method TNN regression predicts differences between two data points and averages predictions from an ensemble of all training data points.
result TNN regression yields more accurate predictions compared to other methods.
Data Twinningstat.ML
Twinning splits data into fast, statistically similar sets.
problem Creating statistically similar data splits for Big Data.
method Twinning is a method based on SPlit for fast, model-independent dataset splitting.
result Twinning is orders of magnitude faster than SPlit.
Framework predicts responses in misspecified systems using GPLFM and BNNs.
problem Predicting responses in dynamical systems with model misspecification.
method Integrates GPLFM and BNNs for uncertainty-aware inference and prediction.
result Systematic propagation of uncertainty from diagnosis to prediction.
Automated digital twin discovery from biological data improves drug discovery and personalized medicine.
problem Developing reliable digital twins from noisy, incomplete biological data.
method Symbolic and sparse regression, Bayesian frameworks, deep learning, and large language models.
result Sparse regression generally outperforms symbolic regression, especially with Bayesian frameworks.
DeepONets improve surrogate modeling for engineering systems.
problem Accurately modeling complex PDEs for engineering systems.
method DeepONets specialize in approximating mathematical operators for PDEs.
result DeepONets achieve high prediction accuracy and zero-shot capability.
The paper introduces twins in Kähler and Sasaki geometry, generalizing known concepts.
problem Understanding twins in Kähler and Sasaki geometry.
method Introducing weighted extremal Kähler twins and extremal Sasaki twins, studying their properties.
result Many twins appear in the Kähler setting and more than one extremal ray in the Sasaki cone.
Minimal Twin Surfacesmath.DG
We report some minimal surfaces that can be seen as copies of a triply periodic minimal surface (TPMS) related by reflections in parallel mirrors. We call them minimal twin surfaces for the resemblance with twin crystal. Brakke's Surface Evolver is employed to construct twinnings of various classical TPMS, including Sc…
Improved aircraft structure prediction using derivative-enhanced sparse Cholesky GP method.
problem Accurate real-time prediction of aircraft structure performance.
method Combining derivative data with a modified dynamic sparse Cholesky linear system solver.
result Improved prediction accuracy of aircraft structure performance.
Virtual twin groups map to symmetric groups, revealing automorphism structure.
problem Understanding homomorphisms between virtual twin groups and symmetric groups.
method Using irreducible right-angled Coxeter groups and right-angled Artin groups.
result A complete description of homomorphisms between virtual twin groups and symmetric groups, including the structure of the automorphism group of .
Some remarks on twin groupsmath.GR
The twin group is a right angled Coxeter group generated by involutions and having only far commutativity relations. These groups can be thought of as planar analogues of Artin braid groups. In this note, we study some properties of twin groups whose analogues are well-known for Artin braid groups. We give…
Smooth manifolds can be triangulated with graphs of bounded twin-width.
problem Understanding the structure of triangulations of smooth manifolds.
method Using Whitney's triangulation method and bounding the twin-width of specific graphs.
result Compact smooth manifolds have triangulations with graphs of bounded twin-width.
Paper presents a deep learning framework for faster, more accurate nuclear reactor power prediction.
problem Inaccurate and inefficient modeling of nuclear reactor transients.
method Hybrid digital twin-focused multi-stage deep learning framework using feed-forward neural networks.
result Achieved remarkable accuracy (96% classification, 2.3% MAPE) with noise-enhanced simulated data.
Digital twin reduces costs in various fields.
problem High costs in decision-making processes.
method Use of digital twin model for cost reduction.
result Digital twin acts as a cost reduction method.
ISOMORPH: A Supply Chain Digital Twin for Simulation, Dataset Generation, and Forecasting Benchmarksstat.ML
ISOMORPH creates a digital twin for supply chain logistics, advancing time-series forecasting benchmarks.
problem Lack of public benchmarks for supply chain logistics time-series forecasting.
method Developed a digital twin simulator with interpretable parameters and modular topology, generating datasets and verifying conservation laws.
result Foundation models achieve MASE values exceeding public benchmarks at low-to-moderate horizons, supporting UQ.
Paper studies pure virtual twin groups and their automorphisms.
problem Understanding the structure and automorphisms of pure virtual twin groups.
method Analyzes stable isotopy classes of immersed circles on surfaces, extending classical knot theory.
result Proves is an irreducible right-angled Artin group with trivial center and gives its precise presentation.
Generative model calibrates 3D battery cathode morphologies from 2D images.
problem Calibrate 3D morphologies of all-solid-state battery cathodes from 2D microscopy images.
method Combining GANs with excursion sets of Gaussian random fields.
result Calibrated digital twins enable systematic exploration of morphological scenarios.
Probabilistic method identifies Purkinje network from ECG data.
problem Challenging task of identifying Purkinje conduction system in heart.
method Bayesian optimization and approximate Bayesian computation for probabilistic identification.
result Generates a population of plausible Purkinje networks fitting ECG within tolerance.
Probabilistic machine learning based predictive and interpretable digital twin for dynamical systemsstat.ML
Proposes a probabilistic digital twin for dynamical systems using sparse Bayesian learning.
problem Creating and updating accurate digital twins for complex dynamical systems.
method Sparse Bayesian machine learning, two approaches: input-output and output-only.
result Identifies correct perturbation terms and associated parameters in dynamical systems.
Deep RL optimizes sensor placement in digital twins for dynamic data acquisition.
problem Limited applicability of traditional sensor placement techniques for online applications.
method Formulates sensor placement as a Markov decision process and uses deep reinforcement learning.
result Improves predictive accuracy and reliability of digital twins through adaptive sensor repositioning.
For spherical Tits buildings of the classical types there are well-known explicit descriptions as flag complexes. Similarly for affine buildings of the classical types there are explicit constructions in terms of lattices. In this article we generalize the flag complex description to twin cities, a generalization of tw…
Digital twin technology has significant promise, relevance and potential of widespread applicability in various industrial sectors such as aerospace, infrastructure and automotive. However, the adoption of this technology has been slower due to the lack of clarity for specific applications. A discrete damped dynamic sy…
Paper introduces probabilistic digital twins for optimal decision making under uncertainty.
problem Optimal sequential decision making under incomplete and uncertain information.
method Formal definition of epistemic uncertainty using measure theory, and solution via deep reinforcement learning.
result Proposes a generic approximate solution for optimal sequential decision making.
Twin-Boot integrates uncertainty estimation into optimization using parallel training of identical models.
problem Uncertainty in overparameterized models, especially in low-data regimes.
method Twin-Bootstrap Gradient Descent (Twin-Boot) trains two identical models on independent bootstrap samples and uses their divergence to guide learning.
result Improves calibration and generalization, yields interpretable uncertainty maps.
The relationship between minimal algebraic Kac-Moody groups and twin buildings is well known as is the relationship between formal completions in one direction and affine buildings. Nevertheless, as the completion of a Kac-Moody group in one direction destroys the opposite BN-pair, there exists no longer a twin buildin…
Digital Twin Calibration for Biological System-of-Systems: Cell Culture Manufacturing Processq-bio.QM
Optimizes biomanufacturing processes with a new digital twin calibration method.
problem Lack of interpretability and sample efficiency in traditional DoE methods.
method Developed a computational approach to calibrate Bio-SoS digital twin model.
result Guides sample-efficient and interpretable DoEs by quantifying sub-model parameter estimation errors.
Enhanced multi-fidelity models improve digital twin accuracy and uncertainty quantification.
problem Lack of detailed application-specific data and inaccurate sensor data hinder surrogate model learning for digital twins.
method Proposes a multi-fidelity surrogate model framework integrating PCFE and GP, and deep-HPCFE with auto-regression schemes.
result Demonstrates improved accuracy and uncertainty quantification in digital twin systems.
Proves Alexander and Markov theorems for higher genus virtual doodles.
problem Classifying isotopy classes of immersed circles on surfaces.
method Introduces virtual twin groups to extend twin groups and prove theorems for higher genus.
result Proves Alexander and Markov theorems for higher genus virtual doodles.
We show that twin building lattices have linear divergence, which implies that all asymptotic cones are without cut-points.
We introduce the category of singular 2-dimensional cobordisms and show that it admits a completely algebraic description as the free symmetric monoidal category on a twin Frobenius algebra, by providing a description of this category in terms of generators and relations. A twin Frobenius algebra (C, W, z, z^*) consist…
On the Optimal Representation Efficiency of Barlow Twins: An Information-Geometric Interpretationcs.LG
This paper analyzes Barlow Twins' representation efficiency using information-geometric methods.
problem Understanding and comparing the efficiency of self-supervised learning methods.
method Introduces an information-geometric framework to quantify representation efficiency and applies it to Barlow Twins.
result Proves that Barlow Twins achieves optimal representation efficiency (η=1).
Novel digital twin for complex systems improves performance.
problem Lack of practical implementation details for stochastic nonlinear MDOF systems.
method Decouples time-scales, uses physics-based model, Bayesian filtering, and machine learning.
result Excellent performance of proposed digital twin framework validated by examples.
New groups and subgroups classified with representations and properties.
problem Classifying representations of new groups and subgroups.
method Introduced new groups and subgroups, classified representations into GL_n(C), investigated properties.
result Classified homogeneous 2-local representations of M_kVT_n into eight distinct types.
The paper characterizes crystallographic groups derived from virtual braid and twin groups.
problem Characterizing crystallographic groups from virtual braid and twin groups.
method Analyzing quotients of virtual braid and twin groups by their commutator subgroups.
result The quotients of virtual braid and twin groups by their commutator subgroups are crystallographic groups.
Paper defines doodles on closed surfaces, unifying classical and virtual theories.
problem Classifying doodles on closed surfaces, especially non-orientable ones.
method Introducing twisted virtual doodles, defining twin groups, and proving Alexander- and Markov-type theorems.
result Unified theory of doodles, showing trivial center and residually finite properties.
A digital twin for multi-scale systems uses physics-based and machine learning models.
problem Lack of application-specific details in digital twin technology.
method Strategically separates into physics-based and data-driven models; uses mixture of experts with Gaussian Process.
result Robust and accurate predictions at future time-steps for multi-scale systems.
Generative models improve digital twins for structures with uncertainties.
problem Uncertainty in structural modelling limits deterministic models.
method Two types of generative models: physics-based SFE and data-driven cGANs.
result Data-driven cGANs outperform physics-based models in nonlinear structures.
Aims to integrate AI and modelling for patient health forecasting.
problem Personalized, precise treatment plans for patients.
method Graph neural network (GNNs) and generative adversarial network (GANs) for probabilistic simulations.
result Demonstrated integration of molecular data for predicting physiological state evolution.
Paper develops efficient Bayesian inference for enzymatic SRNs with LNA metamodel.
problem Bayesian inference for nonlinear SDE-based mechanistic models with partial observations and measurement errors.
method Interpretable Bayesian updating LNA metamodel and efficient posterior sampling.
result Proposed approach demonstrates promising performance in empirical studies.
Adjoint SA speeds up bioprocess parameter learning.
problem Challenges in digital twin development for biomanufacturing.
method Adjoint sensitivity analysis on multi-scale enzymatic reaction networks.
result Resilient sensitivities reveal bioprocess regulatory mechanisms.
New findings about twists in 4-sphere diffeomorphisms.
problem Understanding isotopy classes of diffeomorphisms in 4-sphere.
method Using Cerf theory and twists along Montesinos twins.
result The subgroup of twists along Montesinos twins is trivial or cyclic of order two.
Paper introduces a method to learn physics between digital twins using imperfect models.
problem Learning physics from imperfect data and low-fidelity models.
method Bayesian Hierarchical modeling with physics-informed Gaussian processes.
result Models learning between digital twins are less uncertain than independent models but not over-confident.
Option Pricing of Twin Assetsq-fin.PR
How to price and hedge claims on nontraded assets are becoming increasingly important matters in option pricing theory today. The most common practice to deal with these issues is to use another similar or "closely related" asset or index which is traded, for hedging purposes. Implicitly, traders assume here that the h…
EHR-MPC optimizes sepsis treatment using digital twins and inference-time control.
problem Optimal sepsis treatment policies are contested and difficult to adapt during inference.
method EHR-MPC decouples learning patient dynamics from treatment optimization, enabling inference-time control over learned digital twins.
result EHR-MPC achieves comparable off-policy performance and improved simulation performance compared to RL baselines.